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2017 | OriginalPaper | Buchkapitel

Combining SVD and Co-occurrence Matrix Information to Recognize Organic Solar Cells Defects with a Elliptical Basis Function Network Classifier

verfasst von : Grazia Lo Sciuto, Giacomo Capizzi, Dor Gotleyb, Sivan Linde, Rafi Shikler, Marcin Woźniak, Dawid Połap

Erschienen in: Artificial Intelligence and Soft Computing

Verlag: Springer International Publishing

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Abstract

This paper presents a new methodology based on elliptical basis function (EBF) networks and an innovative feature extraction technique which makes use of the co-occurrence matrices and the SVD decomposition in order to recognize organic solar cells defects. The experimental results show that our algorithm achieves an high accuracy of recognition of 96% and that the feature extraction technique proposed is very effective in the pattern recognition problems that involving the texture’s analysis. The proposed methodology can be used as a tool to optimize the fabrication process of the organic solar cells. All the tests carried out for this work were made by using the organic solar cells realized in the Optoelectronic Organic Semiconductor Devices Laboratory at Ben Gurion University of the Negev.

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Vorheriges Kapitel Porous Silica Templated Nanomaterials for Artificial Intelligence and IT Technologies

Nächstes Kapitel An Intelligent Decision Support System for Assessing the Default Risk in Small and Medium-Sized Enterprises

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Titel: Combining SVD and Co-occurrence Matrix Information to Recognize Organic Solar Cells Defects with a Elliptical Basis Function Network Classifier
verfasst von: Grazia Lo Sciuto
Giacomo Capizzi
Dor Gotleyb
Sivan Linde
Rafi Shikler
Marcin Woźniak
Dawid Połap
Verlag: Springer International Publishing
Buch: Artificial Intelligence and Soft Computing
Print ISBN: 978-3-319-59059-2

Electronic ISBN: 978-3-319-59060-8

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-59060-8_47

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